SoroushVahidi/scidocs huggingface dataset:
Stabilizing function of the diaphragm: dynamic MRI and synchronized spirometric assessment.
(2010)

The aim was to describe diaphragmatic behavior during postural limb activities and examine the ventilatory and stabilizing functions of the diaphragm. Thirty healthy subjects were examined in the supine position using a dynamic MRI system assessed simultaneously with specialized spirometric readings. The diaphragmatic excursions (DEs) were measured at three diaphragmatic points in the sagittal plane; the diaphragm positions (DPs) as related to a reference horizontal baseline were determined. Measurements were taken during tidal breathing (TB) and isometric flexion of upper or lower extremities against external resistance together with TB. Mean DE in both upper and lower postural limb activities was greater compared with the TB condition (P < 0.05), with the effect greater for lower limb activities. Inspiratory DPs in the upper and lower extremity activities were lower compared with TB alone (P < 0.01). Expiratory DP was lower only for lower extremity activities (P < 0.01). DP was most affected at the apex of the crescent and crural (posterior) portion of the diaphragm. DEs correlated strongly with tidal volume (Vt) in all conditions. Changes in DEs relative to the initial value were minimal for upper and lower extremities but were related to lower values of Vt (P < 0.03). Significant involvement of the diaphragm in the limb postural activities was found. Resulting DEs and DPs differed from the TB conditions, especially in lower extremity activities. The differences between the percent changes of DEs vs. Vt found for lower extremity activities were confirmed by both ventilatory and postural diaphragm recruitment in response to postural demands.
A Q-band (40–45 GHz) 16-element phased-array transmitter in 0.18-μm SiGe BiCMOS technology
(2008)

A 16-element phased-array transmitter based on 4-bit RF phase shifters is designed in 0.18-mum SiGe BiCMOS technology for Q-band applications. The phased-array shows 12.5plusmn1.5 dB of power gain per channel at 42.5 GHz for all phase states and the 3-dB gain bandwidth is 40-45.6 GHz. The input and output return loss is less than -10 dB at 37.5-50 GHz. The transmitter also results in les8.8deg of RMS phase error and les1.2 dB of RMS gain error for all phase states at 30-50 GHz. The maximum saturated output power is -2.5plusmn1.5 dBm per channel at 42.5 GHz. The RMS gain variation and RMS phase mismatch between all 16 channels is les0.5 dB and les4.5deg, respectively. The chip consumes 720 mA from a 5 V supply voltage and overall chip size is 2.6times3.2 mm2. To our knowledge, this is the first implementation of a 16-element phased array on a silicon chip with the RF phase shifting architecture at any frequency.
Millimeter-wave CMOS design
(2005)

This paper describes the design and modeling of CMOS transistors, integrated passives, and circuit blocks at millimeter-wave (mm-wave) frequencies. The effects of parasitics on the high-frequency performance of 130-nm CMOS transistors are investigated, and a peak f/sub max/ of 135 GHz has been achieved with optimal device layout. The inductive quality factor (Q/sub L/) is proposed as a more representative metric for transmission lines, and for a standard CMOS back-end process, coplanar waveguide (CPW) lines are determined to possess a higher Q/sub L/ than microstrip lines. Techniques for accurate modeling of active and passive components at mm-wave frequencies are presented. The proposed methodology was used to design two wideband mm-wave CMOS amplifiers operating at 40 GHz and 60 GHz. The 40-GHz amplifier achieves a peak |S/sub 21/| = 19 dB, output P/sub 1dB/ = -0.9 dBm, IIP3 = -7.4 dBm, and consumes 24 mA from a 1.5-V supply. The 60-GHz amplifier achieves a peak |S/sub 21/| = 12 dB, output P/sub 1dB/ = +2.0 dBm, NF = 8.8 dB, and consumes 36 mA from a 1.5-V supply. The amplifiers were fabricated in a standard 130-nm 6-metal layer bulk-CMOS process, demonstrating that complex mm-wave circuits are possible in today's mainstream CMOS technologies.
Fully integrated CMOS power amplifier design using the distributed active-transformer architecture
(2002)

A novel on-chip impedance matching and powercombining method, the distributed active transformer is presented. It combines several low-voltage push–pull amplifiers efficiently with their outputs in series to produce a larger output power while maintaining a 50match. It also uses virtual ac grounds and magnetic couplings extensively to eliminate the need for any offchip component, such as tuned bonding wires or external inductors. Furthermore, it desensitizes the operation of the amplifier to the inductance of bonding wires making the design more reproducible. To demonstrate the feasibility of this concept, a 2.4-GHz 2-W 2-V truly fully integrated power amplifier with 50input and output matching has been fabricated using 0.35m CMOS transistors. It achieves a power added efficiency (PAE) of 41% at this power level. It can also produce 450 mW using a 1-V supply. Harmonic suppression is 64 dBc or better. This new topology makes possible a truly fully integrated watt-level gigahertz range low-voltage CMOS power amplifier for the first time.
A SiGe BiCMOS 16-element phased-array transmitter for 60GHz communications
(2010)

The demonstration of multi-Gb/s links in the 60GHz band has created new opportunities for wireless communications [1,2]. Due to the directional nature of millimeter-wave (mm-Wave) propagation, beam steering enables longer-range non-line-of-sight (NLOS) links at these frequencies. A phased-array architecture is attractive for an integrated 60GHz transmitter (Tx) since it can attain both beam steering and higher EIRP through spatial combining. An all-RF 16-element 40-to-45GHz Tx for satellite applications [3], a 6-element 60GHz Tx with IF-path phase-shift [4], and a bi-directional 4-element 60GHz Tx/Rx with RF phase shifters [5] have been recently demonstrated in silicon. This work presents a fully-integrated phased-array Tx which supports multi-Gb/s NLOS IEEE 802.15.3c links. In addition to beamsteering, the IC has the following major features: an on-chip power sensor at each element, 3 temperature sensors, LO leakage and I/Q phase and amplitude adjustment, front-end OP1dB programmability, and an integrated modulator for pi/2-BPSK/MSK signaling (common mode in 802.15.3c). The IC integrates 2240 NPNs, 323,000 FETs and hundreds of transmission lines and is fabricated in the IBM 8HP 0.12µm SiGe BiCMOS process (fT = 200GHz).
On (Commercial) Benefits of Automatic Text Summarization Systems in the News Domain: A Case of Media Monitoring and Media Response Analysis
(2017)

In this work, we present the results of a systematic study to investigate the (commercial) benefits of automatic text summarization systems in a real world scenario. More specifically, we define a use case in the context of media monitoring and media response analysis and claim that even using a simple query-based extractive approach can dramatically save the processing time of the employees without significantly reducing the quality of their work.
Prediction of Hard Drive Failures via Rule Discovery from AutoSupport Data
(2009)

The ability to accurately predict an impending hard disk failure is important for reliable storage system design. The facility provided by most hard drive manufacturers, called S.M.A.R.T. (selfmonitoring, analysis and reporting technology), has been shown by current research to have poor predictive value. The problem of finding alternatives to S.M.A.R.T. for predicting disk failure is an area of active research. In this paper, we present a rule discovery methodology, and show that it is possible to construct decision support systems that can detect such failures using information recorded in the AutoSupport database. We demonstrate the effectiveness of our system by evaluating it on disks that were returned to NetApp from the field. Our evaluation shows that our system can be tuned either to have a high failure detection rate (i.e., classify a bad disk as bad) or to have a low false alarm rate (i.e., not classify a good disk as bad). Further, our rule-based classifier generates rules that are intuitive and easy to understand, unlike black box techniques.
Modeling urban dynamics through GIS-based cellular automata
(1999)

In urban systems modeling, there are many elaborate dynamic models based on intricate decision processes whose simulation must be based on customized software if their space±time properties are to be explored eectively. In this paper we present a class of urban models whose dynamics are based on theories of development associated with cellular automata (CA), whose data is ®ne-grained, and whose simulation requires software which can handle an enormous array of spatial and temporal model outputs. We ®rst introduce the generic problem of modeling within GIS, noting relevant CA models before outlining a generalized model based on Xie's (1996, A general model for cellular urban dynamics. Geographical Analysis, 28, 350±373) ``dynamic urban evolutionary modeling'' (DUEM) approach. We present ways in which land uses are structured through their life cycles, and ways in which existing urban activities spawn locations for new activities. We de®ne various decision rules that embed distance and direction, density thresholds, and transition or mutation probabilities into the model's dynamics, and we then outline the software designed to generate eective urban simulations consistent with GIS data inputs, outputs and related functionality. Finally, we present a range of hypothetical urban simulations that illustrate the diversity of model types that can be handled within the framework as a prelude to more realistic applications which will be reported in later papers. # 1999 Published by Elsevier Science Ltd. All
Plant Disease Prediction using Image Processing Techniques-A Review
(2016)

Agriculture has become much more than simply a means to feed ever growing populations. It is very important where in more than 70% population depends on agriculture in India. That means it feeds great number of people. The plant diseases effect the humans directly or indirectly by health or also economically. To detect these plant diseases we need a fast automatic way. Diseases are analyzed by different digital image processing techniques. In this paper, we have done survey on different digital image processing techniques to detect the plant diseases.
Peeking Behind the Curtains of Serverless Platforms
(2018)

Serverless computing is an emerging paradigm in which an application’s resource provisioning and scaling are managed by third-party services. Examples include AWS Lambda, Azure Functions, and Google Cloud Functions. Behind these services’ easy-to-use APIs are opaque, complex infrastructure and management ecosystems. Taking on the viewpoint of a serverless customer, we conduct the largest measurement study to date, launching more than 50,000 function instances across these three services, in order to characterize their architectures, performance, and resource management efficiency. We explain how the platforms isolate the functions of different accounts, using either virtual machines or containers, which has important security implications. We characterize performance in terms of scalability, coldstart latency, and resource efficiency, with highlights including that AWS Lambda adopts a bin-packing-like strategy to maximize VM memory utilization, that severe contention between functions can arise in AWS and Azure, and that Google had bugs that allow customers to use resources for free.
Characterizing concept drift
(2015)

Most machine learning models are static, but the world is dynamic, and increasing online deployment of learned models gives increasing urgency to the development of efficient and effective mechanisms to address learning in the context of non-stationary distributions, or as it is commonly called concept drift. However, the key issue of characterizing the different types of drift that can occur has not previously been subjected to rigorous definition and analysis. In particular, while some qualitative drift categorizations have been proposed, few have been formally defined, and the quantitative descriptions required for precise and objective understanding of learner performance have not existed. We present the first comprehensive framework for quantitative analysis of drift. This supports the development of the first comprehensive set of formal definitions of types of concept drift. The formal definitions clarify ambiguities and identify gaps in previous definitions, giving rise to a new comprehensive taxonomy of concept drift types and a solid foundation for research into mechanisms to detect and address concept drift.
Conclusions of Ghaheri's Study That Laser Surgery for Posterior Tongue and Lip Ties Improves Breastfeeding Are Not Substantiated.
(2017)

Editor’s Note: The diagnosis and treatment of tongue and lip ties remains a topic of much and at times heated debate. The following correspondences, which take opposite sides of the argument as to the value of laser surgery, are symptomatic of this still unresolved and hotly contested subject. Breastfeeding Medicine is of the opinion that it should serve as a platform for the presentation of both sides of the issue so as to provide information that will assist clinicians in their management decision.
' s personal copy A comparative study on feature reduction approaches in Hindi and Bengali named entity recognition
(2012)

Features used for named entity recognition (NER) are often high dimensional in nature. These cause overfitting when training data is not sufficient. Dimensionality reduction leads to performance enhancement in such situations. There are a number of approaches for dimensionality reduction based on feature selection and feature extraction. In this paper we perform a comprehensive and comparative study on different dimensionality reduction approaches applied to the NER task. To compare the performance of the various approaches we consider two Indian languages namely Hindi and Bengali. NER accuracies achieved in these languages are comparatively poor as yet, primarily due to scarcity of annotated corpus. For both the languages dimensionality reduction is found to improve performance of the classifiers. A Comparative study of the effectiveness of several dimensionality reduction techniques is presented in detail in this paper. 2011 Elsevier B.V. All rights reserved.
Designing Persian Floral Patterns using Circle Packing
(2012)

In this paper, we present a novel approach toward generating floral patterns. We extract the essence of a pattern aside from its appearance and geometry into combinatorial elements. As a result, existing patterns can be reshaped while preserving their essence. Furthermore, we can create new patterns that adhere to high level concepts such as imperfect symmetry and visual balance. By decomposing floral patterns into a configuration of circles and angles, we can reconstruct this patterns on different surfaces given a conformal mapping.
Object discovery in 3D scenes via shape analysis
(2013)

We present a method for discovering object models from 3D meshes of indoor environments. Our algorithm first decomposes the scene into a set of candidate mesh segments and then ranks each segment according to its “objectness” - a quality that distinguishes objects from clutter. To do so, we propose five intrinsic shape measures: compactness, symmetry, smoothness, and local and global convexity. We additionally propose a recurrence measure, codifying the intuition that frequently occurring geometries are more likely to correspond to complete objects. We evaluate our method in both supervised and unsupervised regimes on a dataset of 58 indoor scenes collected using an Open Source implementation of Kinect Fusion [1]. We show that our approach can reliably and efficiently distinguish objects from clutter, with Average Precision score of .92. We make our dataset available to the public.
MC64: A Web Platform to Test Bioinformatics Algorithms in a Many-Core Architecture
(2011)

New analytical methodologies, like the so-called “next-generation sequencing” (NGS), allow the sequencing of full genomes with high speed and reduced price. Yet, such technologies generate huge amounts of data that demand large raw computational power. Many-core technologies can be exploited to overcome the involved bioinformatics bottleneck. Indeed, such hardware is currently in active development. We have developed parallel bioinformatics algorithms for many-core microprocessors containing 64 cores each. Thus, the MC64 web platform allows executing high-performance alignments (NeedlemanWunsch, Smith-Waterman and ClustalW) of long sequences. The MC64 platform can be accessed via web browsers, allowing easy resource integration into thirdparty tools. Furthermore, the results obtained from the MC64 include timeperformance statistics that can be compared with other platforms.
Research on Fast Charge Method for Lead-Acid Electric Vehicle Batteries
(2009)

Electric vehicle (EV) is environment friendly and high efficient. But the shortages of traction battery limited the rapid development of EV. Battery as a key part of EV has aroused lots of engineers to explore the management method and fast charge method is a key technology of battery management for electric vehicle. Constant current-constant voltage (CC-CV) and multistage constant current-constant voltage (MCC-CV) are two traditional charging ways. According to the dynamic circuit model of Lead-acid battery and fast charge theory, on the basic of CC-CV and MCC-CV method, explored the fast charge method for Lead-acid battery of electric vehicle. Compare experiment result of the fast charge method and traditional method. The two major parameters like temperature rise and the capacity-time ratio are considered in order to compare the result. Lots of experiment result support that the negative pulse could eliminate the polarization effective. The MCC-CV with negative pulse method proves working efficient and practical. Keywords-Fast charge; Electric Vehicle; Lead-acid battery;
Deep neural networks on graph signals for brain imaging analysis
(2017)

Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is important to extract discriminative, low-dimensional intrinsic representation of the recorded data. This work proposes a new method to learn the low-dimensional representations from the noise-degraded measurements. In particular, our work proposes a new deep neural network design that integrates graph information such as brain connectivity with fully-connected layers. Our work leverages efficient graph filter design using Chebyshev polynomial and recent work on convolutional nets on graph-structured data. Our approach exploits graph structure as the prior side information, localized graph filter for feature extraction and neural networks for high capacity learning. Experiments on real MEG datasets show that our approach can extract more discriminative representations, leading to improved accuracy in a supervised classification task.
Lossy compression on IoT big data by exploiting spatiotemporal correlation
(2017)

As the volume of data generated by various deployed IoT devices increases, storing and processing IoT big data becomes a huge challenge. While compression, especially lossy ones, can drastically reduce data volume, finding an optimal balance between the volume reduction and the information loss is not an easy task given that the data collected by diverse sensors exhibit different characteristics. Motivated by this, we present a feasibility analysis of lossy compression on agricultural sensor data by comparing fidelity of reconstructed data from various signal processing algorithms and temporal difference encoding. Specifically, we evaluated five real-world sensor data from weather stations as one of major IoT applications. Our experimental results indicate that Discrete Cosine Transform (DCT) and Fast Walsh-Hadamard Transform (FWHT) generate higher compression ratios than others. In terms of information loss, Lossy Delta Encoding (LDE) significantly outperforms others nonetheless. We also observe that, as compression factor is increased, error rates for all compression algorithms also increase. However, the impact of introduced error is much severe in DCT and FWHT while LDE was able to maintain a relatively lower error rate than other methods.
Knowledge discovery from database Using an integration of clustering and classification
(2011)

Clustering and classification are two important techniques of data mining. Classification is a supervised learning problem of assigning an object to one of several pre-defined categories based upon the attributes of the object. While, clustering is an unsupervised learning problem that group objects based upon distance or similarity. Each group is known as a cluster. In this paper we make use of a large database ‘Fisher’s Iris Dataset’ containing 5 attributes and 150 instances to perform an integration of clustering and classification techniques of data mining. We compared results of simple classification technique (using J48 classifier) with the results of integration of clustering and classification technique, based upon various parameters using WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool. The results of the experiment show that integration of clustering and classification gives promising results with utmost accuracy rate and robustness even when the data set is containing missing values. KeywordsData Mining; J48; KMEANS; WEKA; Fisher’s Iris dataset;
Nonlinear control for output regulation of ball and plate system
(2008)

This paper proposes nonlinear control methods for output regulation of ball and plate system. Positions of the ball are regulated with double feedback loops. Recursive backstepping design is employed for the external feedback loop, while switching control scheme is used in the inner feedback loop. System performance was tuned by backstepping parameters. Simulation results show that the proposed nonlinear control works wells in both stabilization and tracking control. Asymptotical stabilities are also achieved under unknown external disturbance in the experiments.
A Benchmark for Automatic Acral Melanoma Preliminary Screening
(2018)

Despite recent progress in artificial intelligence and computer vision, the lack of data for acral melanoma, a particular type of skin disease under nails, makes it difficult to develop its automatic visual diagnosis system. This paper introduces a large data set of dermoscopic images for acral melanoma, which is annotated by senior dermatologists. It contains 6,066 images of two categories: subungual hematomas and other acral melanoma symptoms of malignant tendency (i.e., just bleeding under nails or more critical disorder that requires treatment). We hope this benchmark data set will encourage further research on acral melanoma recognition and will continue to maintain this data set to better serve it. We address the classification task using various computer vision algorithms from conventional techniques to cutting edge deep learning. Currently, we achieve 0.928 accuracy.